Risk ratios and risk differences are two different measures of association for time-to-event outcomes as observed in cohort studies. Risk ratios are the most commonly presented type of outcome because they are easily estimated from Poisson, logistic, or Cox proportional hazards models. Risk differences, or the difference in absolute risk, while less common, boasts several advantages over the risk ratio in terms of interpretability. For instance, if a 50 year old non-smoking woman's 10 year CVD death risk is 1 (10 events per 1,000 person years) whereas a counterfactually matched 50 year old smoking woman has a 10 year CVD death risk of 2% (20 events per 1,000 person years) the risk difference is 1% but the risk ratio or relative risk is 2 or 100%; people often conflate percentages in this case as a fraction of events to person years $\times 100$ (absolute risk difference) or as a proportional difference (RR1/RR2: unitless).
Absolute risk can definitely be measured even in the presence of censoring. With no censoring or competing risks, logistic regression using the identity link is an additive risk model; the coefficients measure absolute risk difference on the natural scale. Similarly, poisson GLMs with the identity link for the counts using the offset of person-years exposure lead to similar estimates of rate differences on the natural scale. For survival models with censoring, parametric models lead to predictions of risk which can be combined to predict risk differences. For instance, with exponential survival models, the log-rate leads to predicted survival curves at several survival times and the model has efficient CIs. Fully stratified Kaplan Meier curves also provide estimates of risk differences but the calculation of the CIs remains an area of research.
Lastly, survival curves and by extension risk differences can be obtained from Cox models using the smoothed estimates of the baseline hazard function with either the Breslow step estimator or the Schoenfeld residuals.